What AI Reveals About Your Company
The problem isn't technology. It's self-knowledge.
Every operating partner has seen it: the portfolio company that launches an ambitious AI initiative and nine months later has little to show but a pilot that few adopted and never scaled.
The post-mortems always blame the usual suspects: vendor selection, data hygiene, talent gaps. Not that these aren’t part of the problem, but these explanations miss the deeper issue.
The real barrier to AI value creation is brutally simple: most companies don’t really know how they actually operate.
The Gap Between the Deck and the Floor
When you acquire a company, you inherit two versions of it. There's the version in the management presentation—clean process flows, defined decision rights, logical workflows. Then there's the version that exists in practice—held together by institutional knowledge, workarounds, and a handful of people who know where everything actually lives.
This creates a fundamental problem. AI initiatives are typically scoped against the idealized business in slide decks. But they execute against the real one. The gap between those two realities is where most AI projects go to die.
What AI Actually Reveals
Deploying AI into an organization is like running an MRI on operational health. It exposes everything:
Undocumented processes. The workflows that exist only in someone's head, passed down like oral tradition.
Data that doesn't connect. Legacy systems that were never designed to talk to each other, now expected to feed machine learning models.
Decision-making that can't be explained. Historical judgment calls that work but can't be codified because no one has articulated the underlying logic.
Incentive structures that punish efficiency. Middle managers whose value depends on managing the complexity they've created.
Heroic individuals masking systemic failure. The people whose departure would crater entire functions typically possess exactly the domain expertise required for AI transformation to succeed.
None of this appears in typical operational due diligence. But all of it potentially undermines AI value creation, whether the goal is growth or bottom line efficiency.
The Implications for Value Creation
For PE professionals, this reframes the AI opportunity entirely.
The portfolio companies best positioned to capture AI value aren't necessarily those with the most sophisticated technology infrastructure. They're the ones with genuine operational clarity—businesses that actually understand their own workflows, trust their data, and can articulate how decisions get made.
This has several practical implications:
Pre-acquisition: Evaluate operational self-awareness as seriously as you evaluate financials and the technology stack. How well does management understand their own processes? Where does institutional knowledge concentrate? What would break if key people left? Do employees have the foundational skills to work with AI tools? Is there cultural openness to workflow transformation? Can the organization upskill at the pace AI demands?
Post-acquisition: Begin with workforce upskilling alongside operational archaeology. Map what actually happens, not just what's supposed to happen. This diagnostic work is unsexy but essential.
Ongoing oversight: Treat AI readiness as a leading indicator of operational health. Companies that continue to struggle to implement AI are telling you something about deeper dysfunction, often rooted in leadership AI fluency and workforce capability gaps.
The Harder Truth
There's a reason this problem persists. Confronting how a company actually operates—as opposed to how it presents itself—is politically uncomfortable. It exposes years of accumulated and well-intentioned workarounds. It threatens people whose value depends on being the only one who knows how things work. It admits that the org chart is fiction.
But AI can be the catalyst that forces the issue. You can't optimize what you don't understand. You can't automate what you can't articulate. You can't scale what depends on individual heroics.
The Real Opportunity
The firms that generate outsize returns from AI prioritize the unglamorous work: upskilling workers, mapping their reality, restructuring incentives, cleaning data, and documenting decisions.
AI transformation is ultimately an exercise in organizational honesty. Technology is the easy part. The hard part is getting a company to see itself clearly and then having the discipline to rebuild based on what you find. And it starts with people. The firms that treat AI transformation as workforce transformation—building capability, confidence and collaboration from the ground up—create sustainable competitive advantages that pure technology plays cannot match.
That’s where the real value creation happens.
NextAccess Authors: Scott Kosch and Valerie VanDerzee
NextAccess guides organizations through AI transformation by fostering sustainable change that optimizes operational excellence while ensuring individuals are engaged, upskilled, and empowered to flourish. We specialize in helping our clients achieve breakthrough improvements in productivity, efficiency, and quality by unlocking the full potential of their people and capabilities.
Want to learn more?
Message Scott Kosch or Valerie VanDerzee to schedule a complimentary 30-minute consultation to explore how we can help your organization.